Transfer Learning for Image Processing: A Review and Practical Considerations

Abstract

Transfer learning has emerged as a transformative paradigm in deep learning–based image processing, enabling effective knowledge reuse from large-scale pretrained models to domain-specific tasks with limited labelled data. While training deep convolutional neural networks (CNNs) from scratch demands extensive computational resources and massive annotated datasets, transfer learning significantly reduces training time and improves generalization by leveraging previously learned feature representations. This paper presents a comprehensive review of transfer learning models for image processing applications. The research examines key transfer learning methods, such as feature extraction, fine-tuning, and domain adaptation, and offers a comparative assessment of popular pretrained models like VGG, ResNet, Inception, EfficientNet, and Vision Transformers. Furthermore, the paper examines major application domains including medical imaging, agriculture, remote sensing, and industrial inspection. Experimental trends and theoretical insights are discussed to highlight trade-offs between accuracy, computational complexity, and parameter efficiency. Despite its effectiveness, transfer learning faces challenges such as domain shift, model bias propagation, overfitting in small datasets, and limited interpretability. Emerging research directions including hybrid CNN–Transformer models, self-supervised pretraining, lightweight deployment strategies, and federated transfer learning are also explored. The findings suggest that transfer learning remains a critical enabler for scalable and practical image processing systems, bridging the gap between large-scale deep learning research and real-world applications. Emerging research directions including hybrid CNN–Transformer models, self-supervised pretraining, lightweight deployment strategies, and federated transfer learning are also explored. This review synthesizes peer-reviewed studies published between 2010 and 2025, retrieved from major scientific databases including IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar, and categorizes the selected works according to transfer learning strategies, pretrained architectures, and application domains. The comparative analysis indicates that feature extraction is generally effective for small and closely related datasets, whereas fine-tuning provides superior adaptation under moderate domain shifts; CNN-based architectures offer favorable efficiency–accuracy trade-offs in resource-constrained settings, while Vision Transformers demonstrate stronger global representation capability when supported by large-scale pretraining. The findings suggest that transfer learning remains a critical enabler for scalable and practical image processing systems.